class_docstr: Transforms tuples of text into tuples of vector sequences.
docstr: Encodes preprocessed strings into sequences of one-hot indices.
docstr_tok: ["Encodes","preprocessed","strings","into","sequences","of","one-hot","indices","."]
code: def __call__(
      self, data: Tuple[str, str]
  ) -> Tuple[List[int], List[int], List[int]]:
      """
      Encodes preprocessed strings into sequences of one-hot indices.
      """
      text_encoder, text_decoder = data[0], data[1]
      sentences = text_encoder.split("[SEP]")[:-1]
      vec_encoder = []
      sentence_ids = []
      for i, sent in enumerate(sentences):
          sent = sent + "[SEP]"
          vec = self._tokenizer_encoder.encode(
              sent, add_special_tokens=False
          )
          if len(vec_encoder) + len(vec) < self.max_input_len:
              vec_encoder.extend(vec)
              ids = [i % 2] * len(vec)
              sentence_ids.extend(ids)
  
      vec_decoder = self._tokenizer_decoder.encode(text_decoder)
      if self.max_output_len is not None:
          if len(vec_decoder) > self.max_output_len:
              vec_decoder = vec_decoder[: self.max_output_len - 1] + [
                  vec_decoder[-1]
              ]
          else:
              vec_decoder = vec_decoder + [0] * (
                  self.max_output_len - len(vec_decoder)
              )
  
      return vec_encoder, sentence_ids, vec_decoder
  
file_path: headliner/headliner/preprocessing/bert_vectorizer.py
imports: from typing import Tuple, List
  from transformers import BertTokenizer
  from headliner.preprocessing.tokenizer import Tokenizer
label_desc: Use image node_id 2 for calling the BertVectorizer obj's underlying member method code with example usage: obj.__call__(data) and returns: vec_encoder,  sentence_ids,  vec_decoder